Abstract The closed-loop performance of model predictive controllers (MPCs) is highly dependent on the choice prediction models, controller formulation, and tuning parameters. However, models are typically optimized for accuracy, instead performance, MPC done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach automating under uncertainty. particular...

Based on a modified min-max optimization strategy, an improved design of model predictive tracking control (MPC) is proposed to guarantee the performance industrial process systems when networked system suffers from communication faults. Packet losses and uncertainties exist in system, which may deteriorate even cause safety accident. To handle with this problem, extended state space utilized M...

purpose : the aim of this study was to evaluate the effect of adjuvant treatments on contrast sensitivity in patients with clinically significant macular edema (csme) treated with macular photocoagulation (mpc). methods : forty eight eyes from 30 patients with non-proliferative diabetic retinopathy (npdr) and csme were included in a prospective randomized clinical trial between august 2008 and ...

We present a differentiable predictive control (DPC) methodology for learning constrained laws unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit model (MPC). Contrary MPC, does not require supervision by expert controller. Instead, system dynamics is learned the observed system’s dynamics, and neural law optimized offline...

In the era of climate change, and with rapid depletion fossil resources, efficient sustainable transportation systems, such as hybrid electric vehicles (HEVs), are becoming imperative. The cornerstone HEVs is battery technology, in order to extend useful life provide improved performance fuel technology. Model predictive control (MPC) an effective technique for management systems (BMS), which e...

Extending the success of model predictive control (MPC) technologies in embedded applications heavily depends on the capability of improving quadratic programming (QP) solvers. Improvements can be done in two directions: better algorithms that reduce the number of arithmetic operations required to compute a solution, and more efficient architectures in terms of speed, power consumption, memory ...

marginal propensity to consume (mpc) in income groups has a great importance in macroeconomic policy making. but due to some restrictions, such as lack of direct data for income and consumption groups in iran's statistical yearbooks, estimation of mpc for income groups has not been done yet. the purpose of this study is to estimate the mpc for income groups by using the relative permanent incom...

This paper proposes a control parametrization under Model Predictive Controller (MPC) framework for constrained linear discrete time systems with bounded additive disturbances. The proposed approach has the same feasible domain as that obtained from parametrization over the family of time-varying state feedback policies. In addition, the closed-loop system is stable in the sense that the state ...

It has recently been shown that the feedback solution to linear and quadratic constrained Model Predictive Control (MPC) problems has an explicit representation as a piecewise linear (PWL) state feedback. For nonlinear MPC the prospects of explicit solutions are even higher than for linear MPC, since the bene…ts of computational e¢ ciency and veri…ability are even more important. Preliminary st...

Model predictive control (MPC) at each time step minimizes a cost function subject to dynamical constraints to obtain a stabilizing control signal. Further, MPC is one of the few methodologies that can be used to design feedback control for nonlinear dynamical systems taking into consideration of actuator saturations. It can thus serve as a suitable fault tolerant control approach for quad-roto...